Developing a clinical decision support system software prototype that assists in the management of patients with self-harm in the emergency department: protocol of the PERMANENS project.

BACKGROUND: Self-harm presents a significant public health challenge. Emergency departments (EDs) are crucial healthcare settings in managing self-harm, but clinician uncertainty in risk assessment may contribute to ineffective care. Clinical Decision Support Systems (CDSSs) show promise in enhancing care processes, but their effective implementation in self-harm management remains unexplored. METHODS: PERMANENS comprises a combination of methodologies and study designs aimed at developing a CDSS prototype that assists clinicians in the personalized assessment and management of ED patients presenting with self-harm. Ensemble prediction models will be constructed by applying machine learning techniques on electronic registry data from four sites, i.e., Catalonia (Spain), Ireland, Norway, and Sweden. These models will predict key adverse outcomes including self-harm repetition, suicide, premature death, and lack of post-discharge care. Available registry data include routinely collected electronic health record data, mortality data, and administrative data, and will be harmonized using the OMOP Common Data Model, ensuring consistency in terminologies, vocabularies and coding schemes. A clinical knowledge base of effective suicide prevention interventions will be developed rooted in a systematic review of clinical practice guidelines, including quality assessment of guidelines using the AGREE II tool. The CDSS software prototype will include a backend that integrates the prediction models and the clinical knowledge base to enable accurate patient risk stratification and subsequent intervention allocation. The CDSS frontend will enable personalized risk assessment and will provide tailored treatment plans, following a tiered evidence-based approach. Implementation research will ensure the CDSS' practical functionality and feasibility, and will include periodic meetings with user-advisory groups, mixed-methods research to identify currently unmet needs in self-harm risk assessment, and small-scale usability testing of the CDSS prototype software. DISCUSSION: Through the development of the proposed CDSS software prototype, PERMANENS aims to standardize care, enhance clinician confidence, improve patient satisfaction, and increase treatment compliance. The routine integration of CDSS for self-harm risk assessment within healthcare systems holds significant potential in effectively reducing suicide mortality rates by facilitating personalized and timely delivery of effective interventions on a large scale for individuals at risk of suicide.

Health service and psychotropic medication use for mental health conditions among healthcare workers active during the Spain Covid-19 Pandemic - A prospective cohort study using web-based surveys.

Little is known about healthcare workers' (HCW) use of healthcare services for mental disorders. This study presents data from a 16-month prospective cohort study of Spanish HCW (n = 4,809), recruited shortly after the COVID-19 pandemic onset, and assessed at four timepoints using web-based surveys. Use of health services among HCW with mental health conditions (i.e., those having a positive screen for mental disorders and/or suicidal thoughts and behaviours [STB]) was initially low (i.e., 18.2 %) but increased to 29.6 % at 16-month follow-up. Service use was positively associated with pre-pandemic mental health treatment (OR=1.99), a positive screen for major depressive disorder (OR=1.50), panic attacks (OR=1.74), suicidal thoughts and behaviours (OR=1.22), and experiencing severe role impairment (OR=1.33), and negatively associated with being female (OR = 0.69) and a higher daily number of work hours (OR=0.95). Around 30 % of HCW with mental health conditions used anxiolytics (benzodiazepines), especially medical doctors. Four out of ten HCW (39.0 %) with mental health conditions indicated a need for (additional) help, with most important barriers for service use being too ashamed, long waiting lists, and professional treatment not being available. Our findings delineate a clear mental health treatment gap among Spanish HCW.

Exploring long-term breast cancer survivors' care trajectories using dynamic time warping-based unsupervised clustering.

OBJECTIVES: Long-term breast cancer survivors (BCS) constitute a complex group of patients, whose number is estimated to continue rising, such that, a dedicated long-term clinical follow-up is necessary. MATERIALS AND METHODS: A dynamic time warping-based unsupervised clustering methodology is presented in this article for the identification of temporal patterns in the care trajectories of 6214 female BCS of a large longitudinal retrospective cohort of Spain. The extracted care-transition patterns are graphically represented using directed network diagrams with aggregated patient and time information. A control group consisting of 12 412 females without breast cancer is also used for comparison. RESULTS: The use of radiology and hospital admission are explored as patterns of special interest. In the generated networks, a more intense and complex use of certain healthcare services (eg, radiology, outpatient care, hospital admission) is shown and quantified for the BCS. Higher mortality rates and numbers of comorbidities are observed in various transitions and compared with non-breast cancer. It is also demonstrated how a wealth of patient and time information can be revealed from individual service transitions. DISCUSSION: The presented methodology permits the identification and descriptive visualization of temporal patterns of the usage of healthcare services by the BCS, that otherwise would remain hidden in the trajectories. CONCLUSION: The results could provide the basis for better understanding the BCS' circulation through the health system, with a view to more efficiently predicting their forthcoming needs and thus designing more effective personalized survivorship care plans.

Traumatic stress symptoms among Spanish healthcare workers during the COVID-19 pandemic: a prospective study.

AIM: To investigate the occurrence of traumatic stress symptoms (TSS) among healthcare workers active during the COVID-19 pandemic and to obtain insight as to which pandemic-related stressful experiences are associated with onset and persistence of traumatic stress. METHODS: This is a multicenter prospective cohort study. Spanish healthcare workers (N = 4,809) participated at an initial assessment (i.e., just after the first wave of the Spain COVID-19 pandemic) and at a 4-month follow-up assessment using web-based surveys. Logistic regression investigated associations of 19 pandemic-related stressful experiences across four domains (infection-related, work-related, health-related and financial) with TSS prevalence, incidence and persistence, including simulations of population attributable risk proportions (PARP). RESULTS: Thirty-day TSS prevalence at T1 was 22.1%. Four-month incidence and persistence were 11.6% and 54.2%, respectively. Auxiliary nurses had highest rates of TSS prevalence (35.1%) and incidence (16.1%). All 19 pandemic-related stressful experiences under study were associated with TSS prevalence or incidence, especially experiences from the domains of health-related (PARP range 88.4-95.6%) and work-related stressful experiences (PARP range 76.8-86.5%). Nine stressful experiences were also associated with TSS persistence, of which having patient(s) in care who died from COVID-19 had the strongest association. This association remained significant after adjusting for co-occurring depression and anxiety. CONCLUSIONS: TSSs among Spanish healthcare workers active during the COVID-19 pandemic are common and associated with various pandemic-related stressful experiences. Future research should investigate if these stressful experiences represent truly traumatic experiences and carry risk for the development of post-traumatic stress disorder.

The role of PPAR-γ in memory deficits induced by prenatal and lactation alcohol exposure in mice.

Patients diagnosed with fetal alcohol spectrum disorder (FASD) show persistent cognitive disabilities, including memory deficits. However, the neurobiological substrates underlying these deficits remain unclear. Here, we show that prenatal and lactation alcohol exposure (PLAE) in mice induces FASD-like memory impairments. This is accompanied by a reduction of N-acylethanolamines (NAEs) and peroxisome proliferator-activated receptor gamma (PPAR-γ) in the hippocampus specifically in a childhood-like period (at post-natal day (PD) 25). To determine their role in memory deficits, two pharmacological approaches were performed during this specific period of early life. Thus, memory performance was tested after the repeated administration (from PD25 to PD34) of: i) URB597, to increase NAEs, with GW9662, a PPAR-γ antagonist; ii) pioglitazone, a PPAR-γ agonist. We observed that URB597 suppresses PLAE-induced memory deficits through a PPAR-γ dependent mechanism, since its effects are prevented by GW9662. Direct PPAR-γ activation, using pioglitazone, also ameliorates memory impairments. Lastly, to further investigate the region and cellular specificity, we demonstrate that an early overexpression of PPAR-γ, by means of a viral vector, in hippocampal astrocytes mitigates memory deficits induced by PLAE. Together, our data reveal that disruptions of PPAR-γ signaling during neurodevelopment contribute to PLAE-induced memory dysfunction. In turn, PPAR-γ activation during a childhood-like period is a promising therapeutic approach for memory deficits in the context of early alcohol exposure. Thus, these findings contribute to the gaining insight into the mechanisms that might underlie memory impairments in FASD patients.

Making in silico predictive models for toxicology FAIR.

In silico predictive models for toxicology include quantitative structure-activity relationship (QSAR) and physiologically based kinetic (PBK) approaches to predict physico-chemical and ADME properties, toxicological effects and internal exposure. Such models are used to fill data gaps as part of chemical risk assessment. There is a growing need to ensure in silico predictive models for toxicology are available for use and that they are reproducible. This paper describes how the FAIR (Findable, Accessible, Interoperable, Reusable) principles, developed for data sharing, have been applied to in silico predictive models. In particular, this investigation has focussed on how the FAIR principles could be applied to improved regulatory acceptance of predictions from such models. Eighteen principles have been developed that cover all aspects of FAIR. It is intended that FAIRification of in silico predictive models for toxicology will increase their use and acceptance.

Genomic and proteomic biomarker landscape in clinical trials.

The use of molecular biomarkers to support disease diagnosis, monitor its progression, and guide drug treatment has gained traction in the last decades. While only a dozen biomarkers have been approved for their exploitation in the clinic by the FDA, many more are evaluated in the context of translational research and clinical trials. Furthermore, the information on which biomarkers are measured, for which purpose, and in relation to which conditions are not readily accessible: biomarkers used in clinical studies available through resources such as ClinicalTrials.gov are described as free text, posing significant challenges in finding, analyzing, and processing them by both humans and machines. We present a text mining strategy to identify proteomic and genomic biomarkers used in clinical trials and classify them according to the methodologies by which they are measured. We find more than 3000 biomarkers used in the context of 2600 diseases. By analyzing this dataset, we uncover patterns of use of biomarkers across therapeutic areas over time, including the biomarker type and their specificity. These data are made available at the Clinical Biomarker App at https://www.disgenet.org/biomarkers/, a new portal that enables the exploration of biomarkers extracted from the clinical studies available at ClinicalTrials.gov and enriched with information from the scientific literature. The App features several metrics that assess the specificity of the biomarkers, facilitating their selection and prioritization. Overall, the Clinical Biomarker App is a valuable and timely resource about clinical biomarkers, to accelerate biomarker discovery, development, and application.

A continuous in silico learning strategy to identify safety liabilities in compounds used in the leather and textile industry.

There is a widely recognized need to reduce human activity's impact on the environment. Many industries of the leather and textile sector (LTI), being aware of producing a significant amount of residues (Keßler et al. 2021; Liu et al. 2021), are adopting measures to reduce the impact of their processes on the environment, starting with a more comprehensive characterization of the chemical risk associated with the substances commonly used in LTI. The present work contributes to these efforts by compiling and toxicologically annotating the substances used in LTI, supporting a continuous learning strategy for characterizing their chemical safety. This strategy combines data collection from public sources, experimental methods and in silico predictions for characterizing four different endpoints: CMR, ED, PBT, and vPvB. We present the results of a prospective validation exercise in which we confirm that in silico methods can produce reasonably good hazard estimations and fill knowledge gaps in the LTI chemical space. The proposed protocol can speed the process and optimize the use of resources including the lives of experimental animals, contributing to identifying potentially harmful substances and their possible replacement by safer alternatives, thus reducing the environmental footprint and impact on human health.

An integrated precision medicine approach in major depressive disorder: a study protocol to create a new algorithm for the prediction of treatment response.

Major depressive disorder (MDD) is the most common psychiatric disease worldwide with a huge socio-economic impact. Pharmacotherapy represents the most common option among the first-line treatment choice; however, only about one third of patients respond to the first trial and about 30% are classified as treatment-resistant depression (TRD). TRD is associated with specific clinical features and genetic/gene expression signatures. To date, single sets of markers have shown limited power in response prediction. Here we describe the methodology of the PROMPT project that aims at the development of a precision medicine algorithm that would help early detection of non-responder patients, who might be more prone to later develop TRD. To address this, the project will be organized in 2 phases. Phase 1 will involve 300 patients with MDD already recruited, comprising 150 TRD and 150 responders, considered as extremes phenotypes of response. A deep clinical stratification will be performed for all patients; moreover, a genomic, transcriptomic and miRNomic profiling will be conducted. The data generated will be exploited to develop an innovative algorithm integrating clinical, omics and sex-related data, in order to predict treatment response and TRD development. In phase 2, a new naturalistic cohort of 300 MDD patients will be recruited to assess, under real-world conditions, the capability of the algorithm to correctly predict the treatment outcomes. Moreover, in this phase we will investigate shared decision making (SDM) in the context of pharmacogenetic testing and evaluate various needs and perspectives of different stakeholders toward the use of predictive tools for MDD treatment to foster active participation and patients' empowerment. This project represents a proof-of-concept study. The obtained results will provide information about the feasibility and usefulness of the proposed approach, with the perspective of designing future clinical trials in which algorithms could be tested as a predictive tool to drive decision making by clinicians, enabling a better prevention and management of MDD resistance.

Exploring the Association of Cancer and Depression in Electronic Health Records: Combining Encoded Diagnosis and Mining Free-Text Clinical Notes.

BACKGROUND: A cancer diagnosis is a source of psychological and emotional stress, which are often maintained for sustained periods of time that may lead to depressive disorders. Depression is one of the most common psychological conditions in patients with cancer. According to the Global Cancer Observatory, breast and colorectal cancers are the most prevalent cancers in both sexes and across all age groups in Spain. OBJECTIVE: This study aimed to compare the prevalence of depression in patients before and after the diagnosis of breast or colorectal cancer, as well as to assess the usefulness of the analysis of free-text clinical notes in 2 languages (Spanish or Catalan) for detecting depression in combination with encoded diagnoses. METHODS: We carried out an analysis of the electronic health records from a general hospital by considering the different sources of clinical information related to depression in patients with breast and colorectal cancer. This analysis included ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification) diagnosis codes and unstructured information extracted by mining free-text clinical notes via natural language processing tools based on Systematized Nomenclature of Medicine Clinical Terms that mentions symptoms and drugs used for the treatment of depression. RESULTS: We observed that the percentage of patients diagnosed with depressive disorders significantly increased after cancer diagnosis in the 2 types of cancer considered-breast and colorectal cancers. We managed to identify a higher number of patients with depression by mining free-text clinical notes than the group selected exclusively on ICD-9-CM codes, increasing the number of patients diagnosed with depression by 34.8% (441/1269). In addition, the number of patients with depression who received chemotherapy was higher than those who did not receive this treatment, with significant differences (P<.001). CONCLUSIONS: This study provides new clinical evidence of the depression-cancer comorbidity and supports the use of natural language processing for extracting and analyzing free-text clinical notes from electronic health records, contributing to the identification of additional clinical data that complements those provided by coded data to improve the management of these patients.

Development of In Silico Methods for Toxicity Prediction in Collaboration Between Academia and the Pharmaceutical Industry.

The pharmaceutical industry would benefit from the collaboration with academic groups in the development of predictive safety models using the newest computational technologies. However, this collaboration is sometimes hampered by the handling of confidential proprietary information and different working practices in both environments. In this manuscript, we propose a strategy for facilitating this collaboration, based on the use of modeling frameworks developed for facilitating the use of sensitive data, as well as the development, interchange, hosting, and use of predictive models in production. The strategy is illustrated with a real example in which we used Flame, an open-source modeling framework developed in our group, for the development of an in silico eye irritation model. The model was based on bibliographic data, refined during the company-academic group collaboration, and enriched with the incorporation of confidential data, yielding a useful model that was validated experimentally.

ISOTOPE: ISOform-guided prediction of epiTOPEs in cancer.

Immunotherapies provide effective treatments for previously untreatable tumors and identifying tumor-specific epitopes can help elucidate the molecular determinants of therapy response. Here, we describe a pipeline, ISOTOPE (ISOform-guided prediction of epiTOPEs In Cancer), for the comprehensive identification of tumor-specific splicing-derived epitopes. Using RNA sequencing and mass spectrometry for MHC-I associated proteins, ISOTOPE identified neoepitopes from tumor-specific splicing events that are potentially presented by MHC-I complexes. Analysis of multiple samples indicates that splicing alterations may affect the production of self-epitopes and generate more candidate neoepitopes than somatic mutations. Although there was no difference in the number of splicing-derived neoepitopes between responders and non-responders to immune therapy, higher MHC-I binding affinity was associated with a positive response. Our analyses highlight the diversity of the immunogenic impacts of tumor-specific splicing alterations and the importance of studying splicing alterations to fully characterize tumors in the context of immunotherapies. ISOTOPE is available at https://github.com/comprna/ISOTOPE.

The DisGeNET cytoscape app: Exploring and visualizing disease genomics data.

Thanks to the unbiased exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. In parallel, network-based approaches have proven to be essential to understand the molecular mechanisms underlying human diseases. The use of these approaches has been boosted by the abundance of information about disease associated genes and variants, high quality human interactomics data, and the emergence of new types of omics data. The DisGeNET Cytoscape App combines the capabilities of Cytoscape with those of DisGeNET, a knowledge platform based on a comprehensive catalogue of disease-associated genes and variants. The DisGeNET Cytoscape App contains functions to query, analyze, and visualize different network representations of the gene-disease and variant-disease associations available in DisGeNET. It supports a wide variety of applications through its query and filter functionalities, including the annotation of foreign networks generated by other apps or uploaded by the user. The new release of the DisGeNET Cytoscape App has been designed to support Cytoscape 3.x and incorporates novel distinctive features such as visualization and analysis of variant-disease networks, disease enrichment analysis for genes and variants, and analytic support through Cytoscape Automation. Moreover, the DisGeNET Cytoscape App features an API to access its core functionalities via the REST protocol fostering the development of reproducible and scalable analysis workflows based on DisGeNET data.

Mental health impact of the first wave of COVID-19 pandemic on Spanish healthcare workers: A large cross-sectional survey.

INTRODUCTION: Healthcare workers are vulnerable to adverse mental health impacts of the COVID-19 pandemic. We assessed prevalence of mental disorders and associated factors during the first wave of the pandemic among healthcare professionals in Spain. METHODS: All workers in 18 healthcare institutions (6 AACC) in Spain were invited to web-based surveys assessing individual characteristics, COVID-19 infection status and exposure, and mental health status (May 5 - September 7, 2020). We report: probable current mental disorders (Major Depressive Disorder-MDD- [PHQ-8≥10], Generalized Anxiety Disorder-GAD- [GAD-7≥10], Panic attacks, Posttraumatic Stress Disorder -PTSD- [PCL-5≥7]; and Substance Use Disorder -SUD-[CAGE-AID≥2]. Severe disability assessed by the Sheehan Disability Scale was used to identify probable "disabling" current mental disorders. RESULTS: 9,138 healthcare workers participated. Prevalence of screen-positive disorder: 28.1% MDD; 22.5% GAD, 24.0% Panic; 22.2% PTSD; and 6.2% SUD. Overall 45.7% presented any current and 14.5% any disabling current mental disorder. Workers with pre-pandemic lifetime mental disorders had almost twice the prevalence than those without. Adjusting for all other variables, odds of any disabling mental disorder were: prior lifetime disorders (TUS: OR=5.74; 95%CI 2.53-13.03; Mood: OR=3.23; 95%CI:2.27-4.60; Anxiety: OR=3.03; 95%CI:2.53-3.62); age category 18-29 years (OR=1.36; 95%CI:1.02-1.82), caring "all of the time" for COVID-19 patients (OR=5.19; 95%CI: 3.61-7.46), female gender (OR=1.58; 95%CI: 1.27-1.96) and having being in quarantine or isolated (OR= 1.60; 95CI:1.31-1.95). CONCLUSIONS: One in seven Spanish healthcare workers screened positive for a disabling mental disorder during the first wave of the COVID-19 pandemic. Workers reporting pre-pandemic lifetime mental disorders, those frequently exposed to COVID-19 patients, infected or quarantined/isolated, female workers, and auxiliary nurses should be considered groups in need of mental health monitoring and support.

The eTRANSAFE Project on Translational Safety Assessment through Integrative Knowledge Management: Achievements and Perspectives.

eTRANSAFE is a research project funded within the Innovative Medicines Initiative (IMI), which aims at developing integrated databases and computational tools (the eTRANSAFE ToxHub) that support the translational safety assessment of new drugs by using legacy data provided by the pharmaceutical companies that participate in the project. The project objectives include the development of databases containing preclinical and clinical data, computational systems for translational analysis including tools for data query, analysis and visualization, as well as computational models to explain and predict drug safety events.

Flame: an open source framework for model development, hosting, and usage in production environments.

This article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application with a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures since the software supports structural normalization, molecular descriptor calculation, and machine learning model generation using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code, adding unlimited model customization. Models generated with Flame can be easily exported, facilitating collaborative model development. All models are stored in a model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly 3 years of development in collaboration with users from the pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high-quality predictive models based on shared legacy data for assessing the safety of drug candidates.

Comorbidity between Alzheimer's disease and major depression: a behavioural and transcriptomic characterization study in mice.

BACKGROUND: Major depression (MD) is the most prevalent psychiatric disease in the population and is considered a prodromal stage of the Alzheimer's disease (AD). Despite both diseases having a robust genetic component, the common transcriptomic signature remains unknown. METHODS: We investigated the cognitive and emotional behavioural responses in 3- and 6-month-old APP/PSEN1-Tg mice, before ß-amyloid plaques were detected. We studied the genetic and pathway deregulation in the prefrontal cortex, striatum, hippocampus and amygdala of mice at both ages, using transcriptomic and functional data analysis. RESULTS: We found that depressive-like and anxiety-like behaviours, as well as memory impairments, are already present at 3-month-old APP/PSEN1-Tg mutant mice together with the deregulation of several genes, such as Ciart, Grin3b, Nr1d1 and Mc4r, and other genes including components of the circadian rhythms, electron transport chain and neurotransmission in all brain areas. Extending these results to human data performing GSEA analysis using DisGeNET database, it provides translational support for common deregulated gene sets related to MD and AD. CONCLUSIONS: The present study sheds light on the shared genetic bases between MD and AD, based on a comprehensive characterization from the behavioural to transcriptomic level. These findings suggest that late MD could be an early manifestation of AD.

An ensemble learning approach for modeling the systems biology of drug-induced injury.

BACKGROUND: Drug-induced liver injury (DILI) is an adverse reaction caused by the intake of drugs of common use that produces liver damage. The impact of DILI is estimated to affect around 20 in 100,000 inhabitants worldwide each year. Despite being one of the main causes of liver failure, the pathophysiology and mechanisms of DILI are poorly understood. In the present study, we developed an ensemble learning approach based on different features (CMap gene expression, chemical structures, drug targets) to predict drugs that might cause DILI and gain a better understanding of the mechanisms linked to the adverse reaction. RESULTS: We searched for gene signatures in CMap gene expression data by using two approaches: phenotype-gene associations data from DisGeNET, and a non-parametric test comparing gene expression of DILI-Concern and No-DILI-Concern drugs (as per DILIrank definitions). The average accuracy of the classifiers in both approaches was 69%. We used chemical structures as features, obtaining an accuracy of 65%. The combination of both types of features produced an accuracy around 63%, but improved the independent hold-out test up to 67%. The use of drug-target associations as feature obtained the best accuracy (70%) in the independent hold-out test. CONCLUSIONS: When using CMap gene expression data, searching for a specific gene signature among the landmark genes improves the quality of the classifiers, but it is still limited by the intrinsic noise of the dataset. When using chemical structures as a feature, the structural diversity of the known DILI-causing drugs hampers the prediction, which is a similar problem as for the use of gene expression information. The combination of both features did not improve the quality of the classifiers but increased the robustness as shown on independent hold-out tests. The use of drug-target associations as feature improved the prediction, specially the specificity, and the results were comparable to previous research studies.

Thirty-day suicidal thoughts and behaviors among hospital workers during the first wave of the Spain COVID-19 outbreak.

BACKGROUND: Healthcare workers are a key occupational group at risk for suicidal thoughts and behaviors (STB). We investigated the prevalence and correlates of STB among hospital workers during the first wave of the Spain COVID-19 outbreak (March-July 2020). METHODS: Data come from the baseline assessment of a cohort of Spanish hospital workers (n = 5450), recruited from 10 hospitals just after the height of the coronavirus disease 2019 (COVID-19) outbreak (May 5-July 23, 2020). Web-based self-report surveys assessed 30-day STB, individual characteristics, and potentially modifiable contextual factors related to hospital workers' work and financial situation. RESULTS: Thirty-day STB prevalence was estimated at 8.4% (4.9% passive ideation only, 3.5% active ideation with or without a plan or attempt). A total of n = 6 professionals attempted suicide in the past 30 days. In adjusted models, 30-day STB remained significantly associated with pre-pandemic lifetime mood (odds ratio [OR] = 2.92) and anxiety disorder (OR = 1.90). Significant modifiable factors included a perceived lack of coordination, communication, personnel, or supervision at work (population-attributable risk proportion [PARP] = 50.5%), and financial stress (PARP = 44.1%). CONCLUSIONS AND RELEVANCE: Thirty-day STB among hospital workers during the first wave of the Spain COVID-19 outbreak was high. Hospital preparedness for virus outbreaks should be increased, and strong governmental policy response is needed to increase financial security among hospital workers.